Map fusion method suitable for sub-map with few overlapping parts

Abstract

The invention relates to the technical field of map fusion, in particular to a map fusion method suitable for a sub-map with few overlapping parts. The method comprises: firstly, iteratively calculating a rotation matrix R by using point cloud plane extraction, normal vector estimation and normal vector projection angle statistics methods, then iteratively transforming source point clouds by usingthe R, finally, performing three-view projection on the two groups of point clouds to obtain two groups of grey-scale maps, and calculating a translation vector t by using a phase correlation methodfor the grey-scale maps; and finally obtaining a transformation matrix (R, t) between the two groups of point clouds. According to the map fusion method suitable for a sub-map with few overlapping parts, the fusion of the sub-maps under the low overlapping degree can be correctly completed, the error of searching corresponding points by the previous methods is avoided, and the method is suitable for the registration between two groups of large map point clouds with fewer overlapping parts and three axes having rotating angles at the same time.

Description

technical field [0001] The present invention relates to the field of map fusion technology, and more particularly, to a map fusion method suitable for less overlapping parts of sub-maps. Background technique [0002] In the tasks of performing surveying and mapping and exploring unknown environments, it is usually possible to send a single or multiple unmanned vehicles to build maps to improve efficiency; but when there is a lack of location information such as GPS, how to collect single or multiple unmanned vehicles The fusion of the obtained point cloud map data has become a difficult problem. At this time, point cloud map fusion technology is particularly important. [0003] The most commonly used point cloud registration algorithm is the iterative nearest neighbor algorithm represented by ICP. The main step in this type of method is to find the closest point pair in the two sets of point clouds, and according to the estimated transformation relationship (R and t) To ca...

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